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Approximate Bayesian Inference
| Approximate Bayesian Inference |
| Autore | Alquier Pierre |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (508 p.) |
| Soggetto topico |
Research & information: general
Mathematics & science |
| Soggetto non controllato |
bifurcation
dynamical systems Edward–Sokal coupling mean-field Kullback–Leibler divergence variational inference Bayesian statistics machine learning variational approximations PAC-Bayes expectation-propagation Markov chain Monte Carlo Langevin Monte Carlo sequential Monte Carlo Laplace approximations approximate Bayesian computation Gibbs posterior MCMC stochastic gradients neural networks Approximate Bayesian Computation differential evolution Markov kernels discrete state space ergodicity Markov chain probably approximately correct variational Bayes Bayesian inference Markov Chain Monte Carlo Sequential Monte Carlo Riemann Manifold Hamiltonian Monte Carlo integrated nested laplace approximation fixed-form variational Bayes stochastic volatility network modeling network variability Stiefel manifold MCMC-SAEM data imputation Bethe free energy factor graphs message passing variational free energy variational message passing approximate Bayesian computation (ABC) differential privacy (DP) sparse vector technique (SVT) Gaussian particle flow variable flow Langevin dynamics Hamilton Monte Carlo non-reversible dynamics control variates thinning meta-learning hyperparameters priors online learning online optimization gradient descent statistical learning theory PAC–Bayes theory deep learning generalisation bounds Bayesian sampling Monte Carlo integration PAC-Bayes theory no free lunch theorems sequential learning principal curves data streams regret bounds greedy algorithm sleeping experts entropy robustness statistical mechanics complex systems |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576874903321 |
Alquier Pierre
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Condensed-Matter-Principia Based Information & Statistical Measures : From Classical to Quantum
| Condensed-Matter-Principia Based Information & Statistical Measures : From Classical to Quantum |
| Autore | Gadomski Adam |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (166 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
entropy
second law thermodynamics Shannon measure of information information theory surface plasmons fractals quantum plasmonics beyond dipole entanglement electromagnetically induced transparency cross-Kerr nonlinearity Gazeau–Klauder coherent states Helstrom bound chemical computing oscillatory reaction genetic optimization classification problem interacting oscillators Flory–De Gennes exponent conformation of protein albumin non-gaussian chain non-isothermal characteristics Fisher’s test Kullback–Leibler divergence network flow channel probability distribution Shannon information measure cross-entropy drones swarms robustness information classical vs. quantum system condensed matter soft matter complex systems |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Condensed-Matter-Principia Based Information & Statistical Measures |
| Record Nr. | UNINA-9910557512803321 |
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Gadomski Adam
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Uncertainty Quantification Techniques in Statistics
| Uncertainty Quantification Techniques in Statistics |
| Autore | Kim Jong-Min |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (128 p.) |
| Soggetto non controllato |
Kullback–Leibler divergence
geometric distribution accuracy AUROC allele read counts mixture model low-coverage entropy gene-expression data SCAD data envelopment analysis LASSO high-throughput sandwich variance estimator adaptive lasso semiparametric regression ?1 lasso Laplacian matrix elastic net feature selection sea surface temperature gene expression data Skew-Reflected-Gompertz distribution lasso next-generation sequencing BH-FDR stochastic frontier model ?2 ridge geometric mean resampling Gompertz distribution adapative lasso group efficiency comparison sensitive attribute MCP probability proportional to size (PPS) sampling randomization device SIS Yennum et al.’s model ensembles |
| ISBN | 3-03928-547-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404091103321 |
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Kim Jong-Min
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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